Use FloatingPoint constraint insteaad of BinaryFloatingPoint.
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…int`. A follow-up to swiftlang/swift#21673. Regenerated `RawOpsGenerated.swift` using TensorFlow version from `swift/utils/update_checkout/update-checkout-config.json`: http://github.com/tensorflow/tensorflow/tree/c7c0a76f1d6b8ac2057434fbf638b77993c6b88e
dan-zheng
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- Use `FloatingPoint` rather than `BinaryFloatingPoint` to constrain
differentiability.
- Follows from:
- swiftlang#21673
- tensorflow/swift-bindings#11
- Use `@differentiable` where clauses to constrain differentiability
of numeric operations.
- The most common constraint is `where Scalar : FloatingPoint` because
`Tensor` conditionally conforms to `Differentiable where Scalar : FloatingPoint`.
Todos:
- Make more `Tensor` operations differentiable.
- This includes reduction and broadcasting ops.
- This is enabled by `@differentiable` where clause type-checking.
- Use VJP functions instead of adjoint functions.
- I would prefer that this be done in a separate patch, after this patch
adds the correct `@differentiable` where clauses.
- Add tests for newly `@differentiable` `Tensor` operations.
dan-zheng
added a commit
to swiftlang/swift
that referenced
this pull request
Jan 9, 2019
* [AutoDiff] [API] Revamp `@differentiable` usages in stdlib.
- Use `FloatingPoint` rather than `BinaryFloatingPoint` to constrain
differentiability.
- Follows from:
- #21673
- tensorflow/swift-bindings#11
- Use `@differentiable` where clauses to constrain differentiability
of numeric operations.
- The most common constraint is `where Scalar : FloatingPoint` because
`Tensor` conditionally conforms to `Differentiable where Scalar : FloatingPoint`.
- `Tensor` now conditionally conforms to `Differentiable` where
`Scalar : Differentiable & FloatingPoint`.
- Allow `@differentiable` where clause conformance requirements to protocol
composition types.
Todos:
- Make more `Tensor` operations differentiable.
- This includes reduction and broadcasting ops.
- This is enabled by `@differentiable` where clause type-checking.
- Use VJP functions instead of adjoint functions.
- I would prefer that this be done in a separate patch, after this patch
adds the correct `@differentiable` where clauses.
- Add tests for newly `@differentiable` `Tensor` operations.
* [AutoDiff] Make VJP applications use the correct substitution map.
If a custom `@differentiable` attribute defines a VJP and where clause
requirements, VJP applications should use a substitution map involving
those requirements.
Note: more related cases need to be handled, such as `@differentiable`
attributes with where clause requirements but no VJP. These cases will
be handled later.
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A follow-up to swiftlang/swift#21673.
Regenerated
RawOpsGenerated.swiftusing TensorFlow version fromswift/utils/update_checkout/update-checkout-config.json:tensorflow/tensorflow@c7c0a76